Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

• URL: http://arxiv.org/abs/2602.20232v1
• Date: Mon, 23 Feb 2026 18:54:46 GMT
• Title: Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
• Authors: Luca Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge Arturo Campos-Gonzalez-Angulo, Ning Wang, Alexander Zook, Melisa Alkan, Kouhei Nakaji, Taylor Lee Patti, Jérôme Florian Gonthier, Mohammad Ghazi Vakili, Alán Aspuru-Guzik,
• Abstract summary: Density functional theory (DFT) is the most widely used method for calculating molecular properties.<n> Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT.<n>We present the Molecular Orbital Learning (MLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects.
• Score: 34.53033480184221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-equilibrium geometries, despite being trained only on small equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches.

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